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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jingyu Zhang Pian Zhou Yongtao Sun Lailong Luo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

With the rapid development of the distributed network and communication, the Internet of Things (IoT) systems have been applied to all walks of life. Blockchain technology is one of the most popular research fields recently, which can provide a reliable storage solution and solve information security issues for IoT systems. Transaction information is the most critical fundamental data in the blockchain systems, which needs to be verified to avoid being tempered with by malicious nodes in the process of transmission. With the exponential growth of the number of IoT devices, the limitations of the current storage structure put too much pressure on the system nodes. How to improve verification efficiency has become a key challenge. To alleviate this problem, this paper proposes a novel high-performance verification mechanism for data security protection in blockchain-based IoT systems. We design a new storage structure based on Huffman trunk tree (HTT), and conduct the quantitative analysis of transaction weights. Transactions are stored in full-featured devices in the form of Huffman Merkle tree (HMT), and only the content of HTT is saved in lightweight devices. Finally, the performance superiority of our mechanism is proved through theoretical analysis and experimental evaluation. In blockchain-based IoT systems, our mechanism significantly reduces the data transmission cost and computation overhead, effectively improving the efficiency of data verification.

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A HYBRID SWARM INTELLIGENCE APPROACH FOR OPTIMIZING MULTIMODAL LARGE LANGUAGE MODELS DEPLOYMENT IN EDGE-CLOUD-BASED FEDERATED LEARNING ENVIRONMENTS

arXiv

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arXiv 2025年

作者： Rjoub, Gaith Elmekki, Hanae Islam, Saidul Bentahar, Jamal Dssouli, Rachida Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Department of Computer Science Khalifa University Abu Dhabi United Arab Emirates Faculty of Information Technology Aqaba University of Technology Aqaba Jordan

The combination of Federated Learning (FL), Multimodal Large Language Models (MLLMs), and edge-cloud computing enables distributed and real-time data processing while preserving privacy across edge devices and cloud infrastructure. However, the deployment of MLLMs in FL environments with resource-constrained edge devices presents significant challenges, including resource management, communication overhead, and non-IID data. To address these challenges, we propose a novel hybrid framework wherein MLLMs are deployed on edge devices equipped with sufficient resources and battery life, while the majority of training occurs in the cloud. To identify suitable edge devices for deployment, we employ Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) is utilized to optimize the transmission of model updates between edge and cloud nodes. This proposed swarm intelligence-based framework aims to enhance the efficiency of MLLM training by conducting extensive training in the cloud and fine-tuning at the edge, thereby reducing energy consumption and communication costs. Our experimental results show that the proposed method significantly improves system performance, achieving an accuracy of 92%, reducing communication cost by 30%, and enhancing client participation compared to traditional FL methods. These results make the proposed approach highly suitable for large-scale edge-cloud computing systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Thermal and RGB Images Work Better Together in Wind Turbine Damage Detection

arXiv

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arXiv 2024年

作者： Svystun, Serhii Melnychenko, Oleksandr Radiuk, Pavlo Savenko, Oleg Sachenko, Anatoliy Lysyi, Andrii Department of Computer Engineering and Information Systems Khmelnytskyi National University Khmelnytskyi29016 Ukraine Department of Computer Science Khmelnytskyi National University Khmelnytskyi29016 Ukraine Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil46020 Ukraine Department of Informatics Kazimierz Pulaski University of Technology and Humanities in Radom Radom26600 Poland

The inspection of wind turbine blades (WTBs) is crucial for ensuring their structural integrity and operational efficiency. Traditional inspection methods can be dangerous and inefficient, prompting the use of unmanned aerial vehicles (UAVs) that access hard-to-reach areas and capture high-resolution imagery. In this study, we address the challenge of enhancing defect detection on WTBs by integrating thermal and RGB images obtained from UAVs. We propose a multispectral image composition method that combines thermal and RGB imagery through spatial coordinate transformation, key point detection, binary descriptor creation, and weighted image overlay. Using a benchmark dataset of WTB images annotated for defects, we evaluated several state-of-the-art object detection models. Our results show that composite images significantly improve defect detection efficiency. Specifically, the YOLOv8 model’s accuracy increased from 91% to 95%, precision from 89% to 94%, recall from 85% to 92%, and F1-score from 87% to 93%. The number of false positives decreased from 6 to 3, and missed defects reduced from 5 to 2. These findings demonstrate that integrating thermal and RGB imagery enhances defect detection on WTBs, contributing to improved maintenance and reliability. © 2024, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Online traffic-aware linked VM placement in cloud data centers

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Science China(information Sciences) 2020年第7期63卷 182-204页

作者： Liwei LIN David S.L.WEI Ruhui MA Jian LI Haibing GUAN Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Department of Computer & Information Sciences Fordham University

In cloud computing, virtual machine(VM) placement plays a crucial role in data center(DC)management, as different ways of VM placement may require different system resources. As Cisco research reveals that virtualization of DC increases traffic within the DC and causes network bandwidth to become scarce resource, recent researches have been focusing on traffic-aware VM placement. However, previous traffic-aware VM placement schemes treat the VM placement as a static process in that they do not take into account the impact of the current placement decision on the subsequent placement. In this paper,we thus propose a novel online traffic-aware VM placement scheme. Our scheme views VM placement as a context-sensitive dynamic process in that the decision of every step of the placement is made aiming at helping the subsequent steps of placement to reduce the required network bandwidth in the long run. In our scheme, we consider not only inter-VM traffic but also the bandwidth constraint of a physical machine(PM) when making a VM placement decision. To realize our objective, we put those VMs with close end time in the same or close proximity PMs so that when the VMs are terminated, one can make enough room for the future arrivals so as to not only minimize the number of active PMs but also reduce networking costs. We conduct extensive simulations to verify the superiority of our scheme in terms of networking costs and energy consumption. Simulation results show that our scheme outperforms improved-best-fit-decreasing(IBFD) scheme, a revised best-fit version that takes inter-VM traffic into account, by 30%–40% on network cost under various scenarios. Our scheme also promises 10%–25% power savings compared with IBFD.

关键词： linked VMs traffic-aware online VM placement cloud data center energy efficient

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Implementation of Gamified Programming Learning Assistant System

Implementation of Gamified Programming Learning Assistant Sy...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Htoo Htoo Sandi Kyaw Keiichi Kaneko Nobuo Funabiki Wen-Chung Kao Department of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Department of Information and Communication Systems Okayama University Okayama Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Despite the rapid growth of the ICT sector, there is a gap between it and STEM education, resulting in high dropout rates in computer science and engineering programs. This has led to many vacancies in the ICT job market. A major challenge is that traditional teaching methods fail to engage students in difficult and abstract STEM subjects like programming. To overcome this, innovative technology-enhanced learning solutions are needed. In the past, we created a Programming Learning Assistant System (PLAS) to help novice students learn programming. PLAS provides various types of exercises to develop code reading and writing skills at different levels. In this paper, we implement the gamification into PLAS to enhance students' understanding of programming concepts, boost their confidence, spark their interest in STEM fields, and increase engagement in the course through interactive and stimulating scenarios.

关键词： computer languages Codes Education Games Writing Problem-solving Sparks Programming profession Consumer electronics STEM

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Double Pepper: Mediated Human-Robot Interaction and Its Influence on Human Perception

Double Pepper: Mediated Human-Robot Interaction and Its Infl...

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International Conference on Behavior, Economic and Social Computing (BESC)

作者： Lisa Keller Nicholas H. Müller Faculty of Computer Science and Business Information Systems Technical University of Applied Sciences Würzburg-Schweinfurt Würzburg Germany

Remote communication solutions have grown in our globalized world. Combining this area with robotics, telepresence robots enable people to feel more present at distant locations and allow the operator to move freely. Seemingly, there is no difference in perceived human affinity whether a person interacts with another person directly or through a telepresence robot, as a preliminary study demonstrates. In contrast to the preliminary study focusing on affinity towards a human interaction partner, the study in this paper deploys a humanoid robot as interaction partner. Hence, this work aims to explore if human affinity towards a humanoid robot varies if people interact with it directly or through a telepresence robot (so called mediated human-robot interaction). A Double 2 robot was used as telepresence robot and a Pepper as humanoid robot interlocutor. To measure participants’ mood, the Multidimensional Mood State Questionnaire was applied before and after interacting with Pepper. Also, human affinity was measured through a questionnaire. The results indicate that there is no significant difference regarding mood and perceived human affinity whether someone interacts with a humanoid robot in person or through a telepresence robot. However, participants were significantly more awake after interacting in person. Concluding, the field of mediated human-robot interaction can enable new successful use cases e.g. deploying a humanoid robot in a telepresence robot tour as a guide instead of a person. Thereby, neither attendees nor a human guide have to be onsite which is beneficial in health hazard crises such as the corona pandemic.

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SvaVoting: A Novel Secret Sharing-Based Verifiable and Anonymous E-Voting Scheme in Blockchain systems

SvaVoting: A Novel Secret Sharing-Based Verifiable and Anony...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Jingyu Zhang Chenghao Wu Jin Yang Lailong Luo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

E-Voting systems play a vital role in various communities, aiming to enhance the efficiency, transparency, and citizen engagement in elections. However, as technology advances, ensuring the security and reliability of e-voting systems becomes profound. While researchers have explored the integration of blockchain technology to improve e-voting systems, there still are technical challenges, particularly regarding voters’ privacy, ballots’ confidentiality, and system performance. To address these challenges, we propose SvaVoting, an innovative blockchain-based e-voting scheme. SvaVoting utilizes the Borda counting method and the identity-based ring signature based on symmetric primitives to anonymously verify the validity of the voter’s identity and ballot format. In addition, we introduce secret sharing and a Cloud Service Provider (CSP) to conduct the final counting while preserving the privacy of the ballots. All users in the system can verify the results of CSP calculations, thereby meeting security goals such as correctness, unforgeability, anonymity and verifiability. The identity-based ring signature in our scheme provides 128-bit security, and even it can work in a quantum computing environment. Experimental results demonstrate that our solution significantly enhances the security and performance of e-voting systems, offering a practical path forward for future e-voting systems.

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Automation 5.0: The Key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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Transaction Data Management Optimization Based on Multi-Partitioning in Blockchain systems

Transaction Data Management Optimization Based on Multi-Part...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jingyu Zhang Xingxing Hou Yilong Teng Lailong Luo School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China

Unspent Transaction Output (UTXO) is part of the transaction data set, which represents the digital cryptocurrency asset in transaction-based blockchain systems. The data management capability, storage method and occupied space of UTXOs will greatly affect the running efficiency and the verification performance of blockchain systems. Especially, with the popularity of blockchain technology, the relevant UTXO data sets have been growing, and all the stored data can no longer be almost completely stored in memory. How should the UTXO transaction data be stored and managed at this time, it is an urgent issue to be solved in bitcoin-like blockchain systems. This paper provides a blockchain transaction data management optimization mechanism based on multi-partitioning. First, we analyze the influencing factors of transactions through real blockchain data. The proposed method can evaluate the time interval and transaction frequency factors, and use the received information to realize the efficient transaction data storage. In our design, UTXOs with lower likelihood to be used in new transactions will be stored in the disk, and the other UTXOs with higher likelihood to be used in new generated transactions should be stored in the cache. This approach aims to minimize memory consumption for the transaction data sets, accelerate UTXO access time during block verification, and ultimately decrease the overall time required for verification, leading to efficient UTXO transaction data management. Finally, the effectiveness of the proposed optimization mechanism is verified through theoretical analysis and simulation experiments, and the UXTO access time has been reduced compared with state-of-the-art methods.

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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